Endophenotype Network-based Approaches to Prediction and Population-based Validation of in Silico Drug Repurposing for Alzheimers Disease

基于内表型网络的方法对阿尔茨海默病的计算机药物重新利用进行预测和基于群体的验证

• 批准号: 10569077
• 负责人: Feixiong Cheng
• 金额: $ 72.2万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10569077
• 关键词: AD transgenic mice; Acceleration; Address; Adherence; Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease therapy; American; Amyloid; Amyloidosis; Animal Model; Bayesian Modeling; Biological Assay; Biological Availability; Blood - brain barrier anatomy; Brain; Case/Control Studies; Cause of Death; Cells; Central Nervous System; Clinical Trials; Combination Drug Therapy; Combined Modality Therapy; Complement; Complex; Computerized Medical Record; Computers; Data; Databases; Dementia; Disease; Disease Outcome; Drug Combinations; Drug Delivery Systems; Drug Targeting; Drug user; Enhancers; Evaluation; Foundations; Genes; Genome; Heritability; Hi-C; Hippocampus; Human; Human Genetics; In Situ; In Vitro; Incidence; Intelligence; Interdisciplinary Study; Investments; Knowledge; Late Onset Alzheimer Disease; Mediating; Medicine; Methodology; Microglia; Molecular; Multiomic Data; Mutate; Network-based; Neurodegenerative Disorders; Neuroglia; Pathogenesis; Patient Care; Patients; Penetration; Pharmaceutical Preparations; Pharmacoepidemiology; Pharmacologic Substance; Pharmacology; Pharmacotherapy; Physiological; Population; Positioning Attribute; Predisposition; Prevention; Preventive therapy; Proteins; Proteome; Publications; Publishing; Quality of life; Rattus; Records; Regimen; Research Personnel; Resected; Role; System; Tauopathies; Testing; Therapeutic; Transgenic Animals; United States; Validation; Visualization; Work; bioinformatics tool; brain endothelial cell; brain tissue; clinical efficacy; clinically relevant; dementia care; drug development; drug discovery; drug efficacy; drug repurposing; drug testing; effective therapy; efficacious treatment; endophenotype; functional genomics; genetic architecture; genome wide association study; genome-wide; genomic data; human genome sequencing; human interactome; improved outcome; in silico; in vivo; individual patient; informatics tool; innovation; mouse model; multiple omics; novel; novel therapeutics; patient health information; pharmacokinetic model; pharmacologic; population based; predictive tools; promoter; rational design; research and development; risk variant; side effect; single cell sequencing; single-cell RNA sequencing; symptom treatment; tau Proteins; therapeutic development; tool; transcriptome; transgenic model of alzheimer disease; trial design; web app

Although researchers have conducted more than 400 human trials for potential treatments of Alzheimer’s disease (AD) in the last two decades, the attrition rate is estimated at over 99%. Furthermore, the “one gene, one drug, one disease” reductionism-informed paradigm overlooks the inherent complexity of the disease and continues to challenge drug discovery for AD. The predisposition to AD involves a complex, polygenic, and pleiotropic genetic architecture. Recent studies have suggested that AD often has common underlying mechanisms, sharing intermediate endophenotypes with many other complex diseases. These endophenotypes, such as amyloidosis and tauopathy, have essential roles in many neurodegenerative diseases. Systematic identification and characterization of novel underlying pathogenesis and disease modules, more so than mutated genes, will serve as a foundation for generating actionable targets as input for drug repurposing and rational design of combination therapy in AD. Integration of the genome, transcriptome, proteome, and the human interactome are essential for such identification. Given our preliminary results, we posit that network- based identification of novel risk genes and endophenotype modules that share degree between amyloid and tau offer unexpected opportunities for drug therapy in AD comparing to targeting amyloid and tau separately. To address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan with three specific aims. Aim 1 will explore amyloid and tau-mediated endophenotype modules for AD -- We will test the network module hypothesis for amyloid and tau using our recently developed Bayesian framework that integrates multi-omics data (i.e., genome-wide association studies [GWAS] loci, single cell sequencing, and human brain Hi-C data) and the human interactome. Aim 2 will be capable of searching existing drugs and combination therapies for AD using network proximity approaches -- We will emphasize the uses of network proximity approaches (i.e., Genome-wide Positioning Systems network [GPSnet]) to identify repurposable drugs and efficacious combination regimens. This will be accomplished by integrating AD endophenotype module findings, public drug-target databases, the human interactome, and the large-scale patient longitudinal Claims- Electronic Medical Record data (over 200 million patients from the MarketScan database). Aim 3 will evaluate brain penetration and target network engagement for repurposable drugs -- We will use the humanized in vitro blood-brain barrier, resected brain tissues (ex vivo/in situ), and transgenic AD models (i.e., TgF344-AD rats) to experimentally evaluate brain penetration and target network engagement. Evaluation will be based upon network proximity to the AD-related endophenotype modules that are relevant to maximizing efficacy and to minimizing side effects. The successful completion of this project will offer powerful network methodologies and bioinformatics tools for prediction and population-based validation of in silico drug repurposing. It will also allow for the identification of novel repurposable drugs and clinically relevant combination therapies toward AD trials.

尽管研究人员已经对阿尔茨海默氏症的潜在治疗方法进行了400多项人体试验 疾病(AD)在过去的二十年里，估计磨损率超过99%。此外，“一个基因， 一种药物，一种疾病“简化论的信息范式忽视了疾病的内在复杂性和 继续挑战AD的药物发现。阿尔茨海默病的易感性涉及复杂的、多基因的和 多效性遗传结构。最近的研究表明，AD通常有共同的潜在原因 机制，与许多其他复杂疾病共享中间内表型。这些 内表型，如淀粉样变性和肌萎缩侧索硬化症，在许多神经退行性疾病中起着重要作用。 系统地识别和表征新的潜在发病机制和疾病模块，更是如此 而不是突变的基因，将作为产生可操作靶点的基础，作为药物再用途的输入 合理设计AD的综合治疗方案。基因组、转录组、蛋白质组和 人与人之间的相互作用对于这种识别是必不可少的。根据我们的初步结果，我们假设网络- 基于对淀粉样蛋白和淀粉样蛋白之间共有程度的新危险基因和内表型模块的鉴定 与单独靶向淀粉样蛋白和tau相比，tau为AD的药物治疗提供了意想不到的机会。至 为了解决潜在的假设，我们建议建立一个综合的跨学科研究计划， 三个具体目标。目标1将探索AD的淀粉样蛋白和tau介导的内表型模块--我们将测试 使用我们最近开发的贝叶斯框架对淀粉样蛋白和tau蛋白的网络模块假设 整合多组学数据(即全基因组关联研究[GWAS]基因座、单细胞测序和 人脑Hi-C数据)和人类互动组。目标2将能够搜索现有的药物和 使用网络邻近方法治疗AD的综合疗法--我们将强调网络的使用 识别可重复使用药物的邻近方法(即全基因组定位系统网络[GPSnet]) 和有效的联合疗法。这将通过集成AD内表型模块来完成 发现，公共药物靶向数据库，人类互动组，以及大规模的患者纵向声明- 电子病历数据(来自MarketScan数据库的2亿多名患者)。AIM 3将评估 可重复使用药物的大脑渗透和目标网络参与--我们将在 体外血脑屏障、切除的脑组织(体外/原位)和转基因AD模型(即TgF344-AD大鼠) 通过实验评估大脑渗透率和目标网络参与度。评估的基础将是 与AD相关的内表型模块的网络接近，这些模块与最大化疗效和 将副作用降至最低。该项目的成功完成将提供强大的网络方法和 生物信息学工具用于药物再利用的预测和基于群体的验证。它还将允许 用于鉴定新的可重复使用的药物和临床上相关的AD试验的联合疗法。